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S. Taye, "Norm Optimization Techniques for Improved Structural Reanalysis", in B.H.V. Topping, (Editor), "Proceedings of the Fourth International Conference on Civil and Structural Engineering Computing", Civil-Comp Press, Edinburgh, UK, pp 117-124, 1989. doi:10.4203/ccp.9.15.1

Abstract

An approximate structural reanalysis method that is based on a binomial series expansion and that employs operations of matrix scaling and norm minimization is presented in this paper. It is shown that the scaling operation significantly reduces several unfavourable effects of the magnitude and nature of design changes on the prediction quality of the model. Norm minimization provides a very effective and optimal scaling factor in a simplified manner.

The most significant achievement of the work is the development of an effective and efficient reanalysis model that provides near-exact response quantities even when large and strongly dispersed changes are introduced in the design. The potential capacity o f the proposed method of improvement is demonstrated using numerical examples. Graphical interpretation of the concepts is also provided.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 9/10 PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON CIVIL AND STRUCTURAL ENGINEERING COMPUTING Edited by: B.H.V. Topping Paper XV.1 Norm Optimization Techniques for Improved Structural Reanalysis S. Taye Department of Civil Engineering, Technion-Israel Institute of Technology, Haifa, Israel doi:10.4203/ccp.9.15.1 purchase the full-text of this paper Full Bibliographic Reference for this paper S. Taye, "Norm Optimization Techniques for Improved Structural Reanalysis", in B.H.V. Topping, (Editor), "Proceedings of the Fourth International Conference on Civil and Structural Engineering Computing", Civil-Comp Press, Edinburgh, UK, pp 117-124, 1989. doi:10.4203/ccp.9.15.1 Abstract An approximate structural reanalysis method that is based on a binomial series expansion and that employs operations of matrix scaling and norm minimization is presented in this paper. It is shown that the scaling operation significantly reduces several unfavourable effects of the magnitude and nature of design changes on the prediction quality of the model. Norm minimization provides a very effective and optimal scaling factor in a simplified manner. The most significant achievement of the work is the development of an effective and efficient reanalysis model that provides near-exact response quantities even when large and strongly dispersed changes are introduced in the design. The potential capacity o f the proposed method of improvement is demonstrated using numerical examples. Graphical interpretation of the concepts is also provided. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £78 +P&P)
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